Apparatus and method for estimating blood pressure

ABSTRACT

An apparatus for non-invasively estimating blood pressure is provided. Thee apparatus for estimating blood pressure may include a bio-signal measurer configured to measure a bio-signal from a user and a processor configured to estimate blood pressure using the measured bio-signal. The processor may extract a first feature and a second feature from the bio-signal at an extraction time, estimate changes in the first feature and the second feature which have occurred during a time period from a calibration time at which the first feature and the second feature are calibrated to the extraction time at which the first feature and the second feature are extracted, and estimate a blood pressure based on the changes in the first feature and the second feature.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation application of U.S. Ser. No.16/359,519, filed Mar. 20, 2019, which claims priority from KoreanPatent Application No. 10-2018-0096673, filed on Aug. 20, 2018 in theKorean Intellectual Property Office, the disclosures of which areincorporated herein by reference in their entireties.

BACKGROUND 1. Field

Apparatuses and methods consistent with example embodiments relate toestimating blood pressure, and more particularly to estimating bloodpressure based on relative changes in cardiovascular features withrespect to the time of calibration.

2. Description of Related Art

Recently, active research has been conducted on Internet technology(IT)-medical convergence technology, which is a combination of ITtechnology and medical technology, due to the aging populationstructure, rapidly growing medical expenses, and the shortage ofprofessional medical service personnel. In particular, health monitoringsystems have extended care from hospitals to patients' home and officeso that the patients can monitor their health state in daily life.Archetypal examples of bio-signals indicating the individual's healthstatus may include an electrocardiography (ECG) signal, aphotoplethysmogram (PPG) signal, an electromyography (EMG) signal, andthe like. Various bio-signal sensors are being developed to measure suchsignals in daily life. In particular, in the case of a PPG sensor, it ispossible to estimate blood pressure of a human body by analyzing pulsewaveforms in which a cardiovascular status is reflected.

SUMMARY

According to an aspect of an example embodiment, there is provided anapparatus for estimating blood pressure including a bio-signal measurerconfigured to measure a bio-signal from a user, and a processorconfigured to extract a first feature and a second feature from thebio-signal at an extraction time, estimate changes in the first featureand the second feature which have occurred during a time period from acalibration time at which the first feature and the second feature arecalibrated to the extraction time and estimate a blood pressure based onthe changes in the first feature and the second feature.

The first feature may be a cardiac output and the second feature may bea total peripheral resistance.

The processor may acquire, from the bio-signal, at least one ofheartbeat information, information on a shape of a waveform of thebio-signal, area information of the waveform, time and amplitudeinformation at a maximum point of the bio-signal, time and amplitudeinformation at a minimum point of the bio-signal, and amplitude and timeinformation of a constituent pulse waveform of the bio-signal, and mayextract the first feature and the second feature based on the at leastone information.

The processor may estimate mean arterial pressure (MAP), diastolic bloodpressure (DBP), and systolic blood pressure (SBP) based on the firstfeature and the second feature.

The processor may calculate a first difference between an initial valueof the first feature at the calibration time and a changed value of thefirst feature at the extraction time, calculate a second differencebetween an initial value of the second feature at the calibration timeand a changed value of the second feature at the extraction time,calculate a product of the first difference and the second difference,and estimate the blood pressure based on the first difference, thesecond difference, and the product of the first difference and thesecond difference.

The processor may normalize each of the first difference, the seconddifference, and the product of the first difference and the seconddifference, based on at least one of the initial value of the firstfeature and the initial value of the second feature at the calibrationtime, to obtain normalization results, and estimate the blood pressurebased on each of the normalization results.

The processor may apply a weight to each of the normalized results toobtain weighted results, combine the weighted results to obtain acombination result, and estimate the blood pressure by applying ascaling factor to the combination result.

The processor may determine the scaling factor based on at least one ofa reference MAP, a reference SBP, and a reference DBP of the user, whichare measured at the calibration time, and a result of combining thereference SBP and the reference DBP.

The processor may independently estimate MAP, SBP, and DBP by adjustingat least one of the weight and the scaling factor.

The processor may estimate MAP and estimate DBP and SBP based on theestimated MAP and a pulse pressure.

The processor may estimate MAP of the user by adjusting at least one ofthe weight and the scaling factor, and estimate DBP and SBP of the userbased on the MAP, a pulse pressure measured from the bio-signal, and theadjusted at least one of the weight and the scaling factor.

The processor may calculate a first value and a second value based onthe pulse pressure, estimate the DBP based on the MAP and the firstvalue, and estimate the SBP based on the estimated DBP and the secondvalue.

The apparatus may further include a communication interface configuredto, when the user measures reference blood pressure for calibrationthrough an external blood pressure measurement device at the calibrationtime, receive the reference blood pressure from the external bloodpressure measurement device.

The bio-signal measurer may measure a bio-signal for extracting thechanged value of the first feature and the changed value of the secondfeature from the user during measurement of the reference bloodpressure.

The processor may determine whether to perform calibration according topreset criteria, and guide the user to perform calibration when it isdetermined that calibration is needed.

The bio-signal may include one or more of a photoplethysmogram (PPG)signal, an electrocardiography (ECG) signal, an electromyography (EMG)signal, and a ballistocardiogram (BCG) signal.

The bio-signal measurer may include a sensor configured to measure atleast one of the PPG signal, the ECG signal, the EMG signal, and the BCGsignal.

The apparatus may further include an output interface configured tooutput a result of estimating the blood pressure.

According to an aspect of another example embodiment, there is provideda method of estimating blood pressure including acquiring a bio-signalof an object at an extraction time; estimating changes in the firstfeature and second feature which have occurred during a time periodbetween the extraction time and a calibration time at which the firstfeature and the second feature are calibrated; and estimating bloodpressure based on the changes in the first feature and the secondfeature.

The first feature may be a cardiac output and the second feature may bea total peripheral resistance.

The extracting of the first feature and the second feature may includeacquiring, from the bio-signal, at least one information of heartbeatinformation, information on a shape of a waveform of the bio-signal,area information of the waveform, time and amplitude information at amaximum point of the bio-signal, time and amplitude information at aminimum point of the bio-signal, and amplitude and time information of aconstituent pulse waveform of the bio-signal, and extracting the firstfeature and the second feature based on the at least one information.

The estimating the blood pressure comprises estimating mean arterialpressure (MAP), diastolic blood pressure (DBP), and systolic bloodpressure (SBP) based on the changes in the first feature and the secondfeature.

The estimating the changes may include calculating a first differencebetween an initial value of the first feature at the calibration timeand a changed value of the first feature at the extraction time,calculating a second difference between an initial value of the secondfeature at the calibration time and a changed value of the secondfeature at the extraction time, and calculating a product of the firstdifference and the second difference.

The estimating the changes may include normalizing each of the firstdifference, the second difference, and the product of the firstdifference and the second difference based on at least one of theinitial value of the first feature and the initial value of the secondfeature at the calibration time, to obtain normalization results.

The estimating the blood pressure may include applying a weight to eachof the normalized results to obtain weighted results, combining theweighted results to obtain combination results, and estimating the bloodpressure by applying a scaling factor to the combination result.

The method may further include determining the scaling factor based onat least one of a reference MAP, a reference SBP, and a reference DBP,which are measured at the calibration time, and a result of combiningthe reference SBP and the reference DBP.

The estimating of the blood pressure may include independentlyestimating MAP, SBP, and DBP by adjusting at least one of the weight andthe scaling factor.

The estimating the blood pressure may include estimating MAP andestimating the DBP and the SBP based on the estimated MAP and a pulsepressure.

The estimating the blood pressure may include estimating MAP of theobject by adjusting at least one of the weight and the scaling factor,and estimating DBP and SBP of the object based on the MAP, a pulsepressure measured from the bio-signal, and the adjusted at least one ofthe weight and the scaling factor.

The estimating the DBP and the SBP may include calculating a first valueand a second value based on the pulse pressure, estimating the DBP basedon the MAP and the first value, and estimating the SBP based on the DBPand the second value.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and/or other aspects will be more apparent by describingcertain example embodiments, with reference to the accompanyingdrawings, in which:

FIG. 1 is a block diagram illustrating an apparatus for estimating bloodpressure according to one example embodiment;

FIG. 2 is a block diagram illustrating an apparatus for estimating bloodpressure according to another example embodiment;

FIG. 3 is a block diagram illustrating a configuration of a processoraccording to an example embodiment of FIGS. 1 and 2 ;

FIG. 4 is a diagram for describing cardiovascular feature extraction;

FIGS. 5A and 5B are flowcharts illustrating a method of estimating bloodpressure according to an example embodiment; and

FIGS. 6A and 6B illustrate a wearable device according to an exampleembodiment.

DETAILED DESCRIPTION

Example embodiments are described in greater detail below with referenceto the accompanying drawings.

In the following description, like drawing reference numerals are usedfor like elements, even in different drawings. The matters defined inthe description, such as detailed construction and elements, areprovided to assist in a comprehensive understanding of the exampleembodiments. However, it is apparent that the example embodiments can bepracticed without those specifically defined matters. Also, well-knownfunctions or constructions are not described in detail since they wouldobscure the description with unnecessary detail.

It should be noted that in some alternative implementations, thefunctions/acts noted in the blocks may occur out of the order noted inthe flowcharts. For example, two blocks shown in succession may in factbe executed substantially concurrently or the blocks may sometimes beexecuted in the reverse order, depending upon the functionality/actsinvolved.

Terms described in below are selected by considering functions in theembodiment and meanings may vary depending on, for example, a user oroperator's intentions or customs. Therefore, in the followingembodiments, when terms are specifically defined, the meanings of termsshould be interpreted based on definitions, and otherwise, should beinterpreted based on general meanings recognized by those skilled in theart.

As used herein, the singular forms are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprises” and/or “comprising,” or“includes” and/or “including” when used in this description, specify thepresence of stated features, numbers, steps, operations, elements,components or combinations thereof, but do not preclude the presence oraddition of one or more other features, numbers, steps, operations,elements, components or combinations thereof.

Expressions such as “at least one of,” when preceding a list ofelements, modify the entire list of elements and do not modify theindividual elements of the list. For example, the expression, “at leastone of a, b, and c,” should be understood as including only a, only b,only c, both a and b, both a and c, both b and c, all of a, b, and c, orany variations of the aforementioned examples.

It will also be understood that the elements or components in thefollowing description are discriminated in accordance with theirrespective main functions. In other words, two or more elements may bemade into one element or one element may be divided into two or moreelements in accordance with a subdivided function. Additionally, each ofthe elements in the following description may perform a part or whole ofthe function of another element as well as its main function, and someof the main functions of each of the elements may be performedexclusively by other elements. Each element may be realized in the formof a hardware component, a software component, and/or a combinationthereof.

Hereinafter, an apparatus and method for estimating blood pressure willbe described in detail with reference to the accompanying drawings.

FIG. 1 is a block diagram illustrating an apparatus for estimating bloodpressure according to an example embodiment. The apparatus 100 forestimating blood pressure of the present embodiment may be mounted in anelectronic device, such as a smartphone, a tablet personal computer(PC), a desktop PC, a notebook PC, and the like, or may be fabricated asan independent hardware device. In this case, the independent hardwaredevice may be a wearable device of a wristwatch type, a bracelet type, awristband type, a ring type, a glasses-type, or a hairband type.However, the hardware device is not limited to the above examples.

Referring to FIG. 1 , the apparatus 100 for measuring blood pressureincludes a bio-signal measurer 110 and a processor 120.

The bio-signal measurer 110 may include one or more sensors and measurevarious bio-signals from an object of interest through the sensors. Inparticular, the sensors may include at least one light emitter and atleast one light detector to measure a photoplethysmogram (PPG) signal,an electrocardiography (ECG) signal, an electromyography (EMG) signal,and a ballistocardiogram (BCG) signal. However, the sensors may berealized as a spectrometer, but are not limited thereto.

The processor 120 may receive the bio-signal from the bio-signalmeasurer 110 and estimate blood pressure based on the receivedbio-signal. When the processor 120 receives the bio-signal, theprocessor 120 may extract cardiovascular features that affect bloodpressure from the bio-signal, and may estimate blood pressure based onthe extracted cardiovascular features. In particular, the cardiovascularfeatures may include a cardiac output (CO) feature as a first featureand a total peripheral resistance (TPR) feature as a second feature.However, the cardiovascular features are not limited thereto.

The processor 120 may estimate blood pressure using referenceinformation acquired from the object at the time of calibration of thebio-signal. Here, the reference information may include reference bloodpressure measured at the time of calibration, a reference bio-signal,and a reference cardiovascular feature, for example, a first referencefeature and a second reference feature, extracted from the referencebio-signal. In particular, the reference blood pressure may includereference mean arterial pressure (MAP), reference diastolic bloodpressure (DBP), and reference systolic blood pressure (SBP).

For example, when the processor 120 extracts the first feature and thesecond feature from the bio-signal measured by the bio-signal measurer110 in response to a request for estimating blood pressure, theprocessor 120 may estimate relative changes of the first feature and thesecond feature with respect to the first reference feature and thesecond reference feature, respectively, and estimate blood pressurebased on the estimated relative changes of the first feature and thesecond feature.

Also, in addition to the changes of the first feature and the secondfeature, the processor 120 may estimate blood pressure by using valuescalculated from the change of the first feature and the change of thesecond feature and/or by using other various predefined values togetherwith the changes of the first feature and the second feature. In thiscase, the values calculated from the changes of the first feature andthe second feature may be obtained by multiplying a value indicating thechange of the first feature and a value indicating the change of thesecond feature. Also, the various predefined values may be optimizedaccording to the type of blood pressure (e.g., MAP, DBP, or SBP) to beestimated and/or features of each user.

The processor 120 may independently estimate the MAP, the DBP, and theSBP by adjusting such various values. Alternatively, the processor 120may first estimate MAP, and then estimate DBP and SBP using theestimated MAP and additional information, such as pulse pressure.

FIG. 2 is a block diagram illustrating an apparatus for estimating bloodpressure according to another example embodiment.

Referring to FIG. 2 , the apparatus 200 for estimating blood pressuremay include a bio-signal measurer 110, a processor 120, a communicationinterface 210, an output interface 220, and a storage 230.

The processor 120 may control the bio-signal measurer 110, thecommunication interface 210, and the storage 230 in response to acalibration request or a blood pressure estimation request.

For example, when the calibration request is received from the user or apreset calibration condition is satisfied, the processor 120 may controlthe bio-signal measurer 110 to measure a bio-signal from a user. Thebio-signal measurer 110 may measure the bio-signal from the user underthe control of the processor 120 and extract a first reference featureand a second reference feature that serve as references for bloodpressure estimation from the measured bio-signal.

In addition, when the calibration request is received from the user orthe preset calibration condition is satisfied, the processor 120 mayalso control the communication interface 210 to communicate with anexternal blood pressure measurement device 250. In this case, thecalibration condition may be stored beforehand in the storage 230. Forexample, it may be preset to perform calibration when a predeterminedinterval arrives, the number of times that a blood pressure estimatedvalue falls outside a predetermined normal range is greater than orequal to a threshold, or the blood pressure estimated value fallsoutside the normal range consecutively more than a predetermined numberof times.

The communication interface 210 may communicate with the external bloodpressure measurement device 250 under the control of the processor 120,and when the user completes blood pressure measurement through theexternal blood pressure measurement device 250 in order to performcalibration, the communication interface 210 may receive the measuredblood pressure information as reference blood pressure.

In this case, the communication interface 210 may communicate with theexternal blood pressure measurement device 250 using Bluetoothcommunication, Bluetooth low energy (BLE) communication, near fieldcommunication (NFC), a wireless local area network (WLAN) communication,ZigBee communication, infrared data association (IrDA) communication,Wi-Fi direct (WFD) communication, ultra-wideband (UWB) communication,Ant+ communication, WiFi communication, radio frequency identification(RFID) communication, 3rd generation (3G) communication, 4Gcommunication, 5G communication, etc. However, these are merely examplesand the types of communication are not limited thereto.

The processor 120 may store the first reference feature and secondreference feature extracted for calibration and the reference bloodpressure received from the external blood pressure measurement device250 in the storage 230 as reference information for blood pressuremeasurement.

Also, when a request for estimating blood pressure is received from theuser or the external device 250, the processor 120 may control thebio-signal measurer 110 to measure a bio-signal of the user for bloodpressure measurement. When the bio-signal measurer 110 completes themeasurement of blood pressure, the processor 120 may estimate a relativechange of a cardiovascular feature at the time of estimating bloodpressure with respect to the time of calibration by reading referencedata at the time of calibration, and estimate blood pressure based onthe estimated relative change of cardiovascular feature.

In an example embodiment, when the processor 120 extracts the firstfeature and the second feature from the bio-signal at an extraction timeT_(extraction), and calibrates the first features and the second featureat a calibration time T_(calibration), the processor 120 may estimate achange in the first feature and a change in the second feature whichhave occurred during a time period from the calibration timeT_(calibration) at which the first feature and the second feature arecalibrated, to the extraction time T_(extraction) at which the firstfeature and the second feature are extracted from the bio-signal

When the blood pressure estimation is completed, the communicationinterface 210 may transmit the bio-signal measurement result and/or theblood pressure estimation result to the external device 250, such as asmartphone, a tablet PC, a desktop PC, a notebook PC, a device of amedical institution, or the like.

The output interface 220 may output the bio-signal, the blood pressureestimation result, and additional information associated with the bloodpressure estimation result. For example, the output interface 220 mayvisually provide a variety of information to the user through a displayscreen. For example, when the blood pressure estimation result isdisplayed, if the estimated blood pressure falls outside a predeterminednormal range, warning information may be displayed to the user byhighlighting the result in red color. In another example, a variety ofinformation may be provided to the user in a non-visual manner, such assound, vibration, tactile sensation, or the like, through a speaker, ahaptic motor, or the like. For example, DBP and SBP may be informed byvoice. When the estimated blood pressure falls outside the predeterminednormal range, the user may be informed of abnormality in healthcondition through vibration or tactile sensation.

In addition to the above-described reference information, otherreference information, the bio-signal, and/or the blood pressureestimation result may be stored in the storage 230. For example, otherreference information may include user characteristic information, suchas age, sex, health state, or the like of the user, and information,such as a blood pressure estimation equation.

Meanwhile, the storage 230 may include storage media, such as flashmemory, hard disk, multimedia card micro type memory, card type memory(e.g., SD or XD memory), random access memory (RAM), static randomaccess memory (SRAM), read-only memory (ROM), electrically erasableprogrammable read-only memory (EEPROM), magnetic memory, magnetic disk,and optical disk, but is not limited thereto.

An amount of change in mean arterial pressure (MAP) is determined to beproportional to a CO and a TPR as shown in Equation 1.

ΔMAP=CO×TPR  (1)

Here, ΔMAP represents a mean arterial pressure difference between theleft ventricle and the right ventricle. Generally, mean rightventricular pressure does not exceed 3 to 5 mmHg and may have a similarvalue to mean left ventricular pressure or mean brachial blood pressure.Therefore, when an absolute CO value and an absolute TPR value areknown, it is possible to obtain mean arterial pressure or mean brachialblood pressure. However, it is not easy to estimate the absolute COvalue and absolute TPR value based on a bio-signal. According to thepresent embodiment, it may be possible to estimate an amount of changein blood pressure based on the relative changes in CO and TPR featureswith respect to the time of calibration.

Values within a range of 0.5 to 0.7 plus/minus from the MAP calculatedas described above may be used as the SBP and the DBP. However, the SBPand the DBP may exhibit a decoupling phenomenon in which they do notfollow the tendency of change in MAP according to the mechanism of bloodpressure change. In addition, in a case where the CO or TPR is greatlychanged, such as in a high intensity exercise, the accuracy of bloodpressure estimation rapidly deteriorates so that an error of theestimated blood pressure can be greatly increased. Therefore, it may berequired to estimate blood pressure by taking into account influence ofthe mechanism of blood pressure change in order to improve the accuracyof blood pressure estimation. According to the present embodiment, itmay be possible to estimate blood pressure based on a relative change inCO and TPR features with respect to the time of calibration, which willbe described in detail below, and to stably estimate blood pressure byapplying various factors optimized according to the user'scharacteristics.

FIG. 3 is a block diagram illustrating a configuration of a processoraccording to an example embodiment of FIGS. 1 and 2 . FIG. 4 is adiagram for describing cardiovascular feature extraction. An embodimentof blood pressure estimation performed by the processor 300 will bedescribed with reference to FIGS. 3 and 4 .

Referring to FIG. 3 , the processor 300 may include a feature extractor310, a calibrator 320, a feature change estimator 330, and a bloodpressure estimator 340.

The feature extractor 310 may extract cardiovascular features fromvarious bio-signals measured from a user. In this case, thecardiovascular features may include a first feature including a COfeature and a second feature including a TPR feature.

For example, the feature extractor 310 may acquire heartbeat informationfrom bio-signals, a shape of a bio-signal waveform, the time andamplitude at a maximum point of a bio-signal, the time and amplitude ata minimum point of a bio-signal, the area of a bio-signal waveform, anelapsed time of a bio-signal, the amplitude and time information of aconstituent pulse waveform of a bio-signal, and characteristic pointinformation, such as information on internal division points of piecesof the obtained information, and may extract the features using theacquired characteristic point information.

FIG. 4 is a graph illustrating an example of a pulse wave signal amongthe bio-signals obtained from the user. An example in which the featureextractor 310 extracts features from a pulse wave signal PS will bedescribed with reference to FIG. 4 .

As shown in FIG. 4 , a pulse wave signal may be formed by a summation ofa propagation wave propagating from the heart to peripheral parts and/orblood vessel bifurcations of a body and reflection waves returning fromthe peripheral parts and/or blood vessel bifurcations. In FIG. 4 , awaveform of the measured pulse wave signal is a summation of fiveconstituent pulses, for example, a propagation wave fw and reflectionwaves rw1, rw2, rw3, and rw4.

The feature extractor 310 may obtain characteristic points from thepulse wave signal by analyzing waveforms of the constituent pulses fw,rw1, rw2, rw3, and rw4. For example, feature extractor 310 may extractthe first three constituent pulses fw, rw1, and rw2 to estimate bloodpressure based on the three constituent pulses fw, rw1, and rw2. Thesubsequent pulses may not be observed in some users, may be difficult todetect due to noise, or may often have low correlation with bloodpressure estimation.

For example, times T₁, T₂, and T₃ and amplitudes P₁, P₂, and P₃ ofmaximum points of the first to third constituent pulse waveforms fw,rw1, and rw2 may be obtained as characteristic points. In this case,when a pulse wave signal is obtained, a second-order derivative of theobtained pulse wave signal is computed and the times T₁, T₂, and T₃ andamplitudes P₁, P₂, and P₃ of maximum points of the constituent pulsewaveforms fw, rw1, and rw2 may be obtained using the obtainedsecond-order derivative signal. For example, local minimum points aresearched from the second-order derivative signal to extract times T₁,T₂, and T₃ corresponding to the first to third local minimum points andthe amplitudes P₁, P₂, and P₃ corresponding to the extracted times T₁,T₂, and T₃ may be extracted from the pulse wave signal. Here, the localminimum point refers to a specific point observed in part of asecond-order derivative signal at which the signal decreases and thenincreases again, that is, a downward convex point. However, theembodiment is not limited thereto, such that local maximum points aresearched in the second-order derivative signal and times and amplitudescorresponding to the found local maximum points may be used ascharacteristic points.

In another example, the feature extractor 310 may obtain time T_(max)and amplitude P_(max) at a point in a predetermined period of the pulsewave signal at which the amplitude is maximum as the characteristicpoints. In this case, the predetermined period may refer to a periodfrom the beginning of the pulse wave signal to a point where thedictoric notch (DN) occurs, which indicates a blood pressure systolicperiod.

In another example, the feature extractor 310 may obtain time durationPPG_(dur) indicating the total measurement time of the bio-signal or thearea PPGarea of the bio-signal waveform as the characteristic points. Inthis case, the area of the bio-signal waveform may refer to the totalarea of the bio-signal or the area of the bio-signal corresponding to apredetermined ratio (e.g., 70%) of the entire time duration PPG_(dur).

In still another embodiment, the feature extractor 310 may extract aninternally dividing point between two or more characteristic points asthe characteristic points. Unstable waveforms of the pulse wave signalmay be generated due to non-ideal environment, such as motion noise,sleep, and the like so that the characteristic points may be extractedfrom wrong positions. In this case, blood pressure measurement may besupplemented by utilizing an internally dividing point between theerroneously extracted characteristic points.

For example, when characteristic points (T₁, P₁) and (T_(max), P_(max))are obtained from the blood pressure systolic period, it is possible toobtain an internally dividing point (T_(sys), P_(sys)) between the twocharacteristic points (T₁, P₁) and (T_(max), P_(max)). In this case,weights are applied to time values T1 and Tmax of the two characteristicpoints (T₁, P₁) and (T_(max),P_(max)), time T_(sys) of the internallydividing point may be obtained using the weighed time values, and anamplitude P_(sys) corresponding to the time T_(sys) of the internallydividing point may be extracted. However, the embodiment is not limitedthereto, such that, through the analysis of the obtained bio-signalwaveform, an internally dividing point between characteristic points(T₁, P₁) and (T₂, P₂) related to the first and second constituent pulsewaveforms fw and rw₁ may be obtained from the blood pressure systolicperiod and an internally dividing point between characteristic points(T₃, P₃) and (T₄, P₄) related to the third and fourth consistent pulsewaveforms rw₂ and rw₃ from the blood pressure diastolic period may beobtained.

The feature extractor 310 may extract a first feature and a secondfeature by combining various characteristic points obtained from thebio-signal as described above. For example, the first feature and thesecond feature may be extracted by performing multiplication, division,addition, subtraction, or a combination thereof on the plurality ofcharacteristic points. Alternatively, the first feature and the secondfeature may be extracted using a function that uses, as an input value,a result of multiplication, division, addition, subtraction, or acombination thereof on the plurality of characteristic points. Here, thefunction may be a linear function, a quadric function, anothermulti-dimensional function, a log function, or an exponential function.It is apparent that other types of function can be used. In anotherexample, the first feature and the second feature may be extracted usinga function that has at least one characteristic point as an input value.However, the embodiment is not limited thereto.

Meanwhile, the CO feature and the TPR feature may be extracted bycombining the characteristic points differently according to thecharacteristics of the user. In addition, the CO feature and the TPRfeature may be individually extracted in accordance with the type ofblood pressure by combining the characteristic points differentlyaccording to the blood pressure to be extracted, for example, MBP, DBP,and SBP.

When a user's calibration request is received or the preset calibrationcondition is satisfied as described above, the calibrator 320 maydetermine whether the calibration has been performed by referring to thecalibration condition and, when the calibration is needed, may performcalibration. In this case, when the preset calibration condition issatisfied, the calibrator 320 may guide the user to perform calibration.

When the user measures reference blood pressure through an externalblood pressure measurement device 250 for the calibration, thecalibrator 320 may obtain the reference blood pressure from the externalblood pressure measurement device 250. In addition, the calibrator 320may control the bio-signal measurer 110 to measure a bio-signal forcalibration in response to the user's calibration request.

When the feature extractor 310 extracts the first feature and the secondfeature from the bio-signal measured at the time of calibration, thecalibrator 320 may receive the first feature and the second feature fromthe feature extractor 310 as a first reference feature and a secondreference feature for blood pressure estimation.

The calibrator 320 may store the obtained reference blood pressure, thefirst reference feature, and the second reference feature, in thestorage 230 as reference information for blood pressure estimation, andcalibrate an offset in the blood pressure estimation equation using thereference information.

When the bio-signal measurer 110 measures a bio-signal according to theuser's blood pressure estimation request or the preset criteria and thefeature extractor 310 extracts the first feature and the second featurefor blood pressure estimation from the bio-signal, the feature changeestimator 330 may estimate a relative change in each of the firstfeature and the second feature at the time of blood pressure estimationwith respect to the time of calibration by utilizing the reference bloodpressure, the first reference feature, and the second reference feature,acquired by the calibrator 320. For example, the feature changeestimator 330 may estimate the changes in the first feature and thesecond feature which have occurred during a time period from acalibration time at which the first feature and the second feature arecalibrated by the calibrator 320, to an extraction time at which thefirst feature and the second feature are extracted from the bio-signal.

In one example, the feature change estimator 330 may calculate a firstchange mount that is an amount of change in the first feature at thetime of blood pressure estimation with respect to the first feature atthe time of calibration. In addition, the feature change estimator 330may calculate a second change amount that is an amount of change in thesecond feature at the time of blood pressure estimation with respect tothe second feature at the time of calibration. Also, a third changeamount may be calculated using the calculated first and second changeamounts. For example, the third change amount may be calculated bymultiplying the first change amount and the second change amount. Inthis case, the third change amount may be a factor for correcting anamount of change in blood pressure that cannot be reflected only by thefirst feature and the second feature in a blood-pressure changingsituation, such as a high-intensity aerobic exercise.

In another example, the feature change estimator 330 may normalize thefirst change amount, the second change amount, and the third changeamount to obtain a first change rate, a second change rate, and a thirdchange rate, respectively. In this case, the feature change estimator330 may normalize each change amount based on at least one of the firstfeature and the second feature at the time of calibration. For example,the first change amount may be normalized based on the first referencefeature, the second change amount may be normalized based on the secondreference feature, and the third change amount may be normalized usingthe first reference feature and the second reference feature.

The blood pressure estimator 340 may estimate an amount of change inblood pressure based on the relative changes in the first feature andthe second feature estimated by the feature change estimator 330. Forexample, the amount of change in blood pressure may be estimated bycombining the first change amount, second change amount, and thirdchange amount calculated by the feature change estimator 330.Alternatively, the amount of change in blood pressure may be estimatedby combining the first change rate, second change rate, and third changerate calculated by the feature change estimator 330. In this case, aweight may be applied to each of the change amounts or each of thechange rates and then the weighted change amounts or change rates may becombined, and a scaling factor may be additionally applied to thecombination result, thereby acquiring a blood pressure measurementresult in which the user-specific feature has been reflected.

For example, as shown in Equation 2, the blood pressure estimator 340may apply a weight to each of the first change rate, the second changerate, and the third change rate, linearly combine the weighted rates,and estimate the amount of change in blood pressure by applying ascaling factor to the linear combination result.

ΔBP=SF _(ad)×(αΔf1_(n) =βΔf2_(n) +γΔf3_(n))  (2)

Here, ΔBP represents an estimated amount of change in blood pressure,and may be MAP, DBP, and SBP. Δf1_(n), Δf2_(n), and Δf3_(n) representthe first change rate, the second change rate, and the third changerate, respectively. α, β, and γ represent a weight to be applied to therespective change rates, and may be defined according to the type ofblood pressure to be estimated and/or the characteristic of the user. Inaddition, SF_(ad) represents a scaling factor defined adaptivelyaccording to the user's characteristic and/or the type of blood pressureto be estimated. For example, the scaling factor may be a valuecalculated by combining two or more of reference MAP, reference DBP andreference SBP measured by the external blood pressure measurement device250 at the time of calibration and a combination thereof.

Meanwhile, according to one example embodiment, the blood pressureestimator 340 may independently estimate an amount of change in each ofMAP, DBP, and SBP using Equation 2 above. For example, the amount ofchange in each blood pressure may be independently estimated using theamount of change or the change rate of each of the first feature and thesecond feature extracted by the feature extractor 310 according to thetype of blood pressure. Alternatively, a weight applied to the amount ofchange or change rate of each of the features and/or the scaling factorto be applied to the combination result of the amounts of change or thechange rates may be set differently according to the type of bloodpressure so that the amount of change in each of MAP, DBP, and SBP maybe independently estimated. For example, in order to estimate the MAP, areference MAP of the corresponding user may be used as a scaling factor.Similarly, the reference DBP and the reference SBP may be used asscaling factors for estimating the DBP and the SBP.

In another example, the blood pressure estimator 340 may sequentiallyestimate the MAP, the DBP, and the SBP. For example, the blood pressureestimator 340 may first estimate an amount of change in MAP usingEquation 2 above, and estimate the DBP and SBP using a MAP estimateobtained based on the amount of change in MAP. In this case, the bloodpressure estimator 340 may estimate the DBP and the SBP using a pulsepressure along with the MAP estimate. For example, the blood pressureestimator 340 may calculate a first value and a second value based onthe pulse pressure, estimate DBP using the MAP estimate and the firstvalue, and estimate SBP using the DBP and the second value.

Equation 3 below shows examples of a function for estimating DBP basedon the MAP estimate and a pulse pressure.

$\begin{matrix}{{DBP} = {{MAP} - \frac{PP}{3}}} & (3)\end{matrix}$${DBP} = {{MAP} - {0.01 \times {\exp\left( {4.14 - \frac{40.74}{HR}} \right)} \times PP}}$$\begin{matrix}{{SBP} = {{DBP} + {PP}}} & (4)\end{matrix}$

Here, MAP represents mean arterial pressure, DBP represents diastolicblood pressure, and SBP represents systolic blood pressure. In addition,PP represents a pulse pressure, and HR represents a heart rate. Here, afirst value subtracting from the MAP and a second value adding to theDBP are not limited to the above examples, and may be defined variouslyin consideration of the user's characteristic. The pulse pressure maycorrespond to the difference between the systolic and diastolic bloodpressure, and may represent the force that the heart generates each timeit contracts. The pulse pressure may be a previously obtained value. Forexample, the pulse pressure may be a reference value which is previouslyobtained by using the estimated SBP and DBP at a calibration time, byusing other bio-signal, or by using a pulse pressure measuring device.

Meanwhile, the blood pressure estimator 340 may acquire a pulse pressureby analyzing the measured bio-signal, for example, a pulse wave signal.Alternatively, the blood pressure measurer 340 may receive a measuredpulse pressure from a pulse pressure measurement device or may use apreset reference pulse pressure of the user.

When the amount of change in blood pressure is estimated, the bloodpressure estimator 340 may estimate blood pressure using a function suchas Equation 5 below.

BP _(est) =BP _(cal) +ΔBP  (5)

Here, BP_(est) represents a blood pressure estimate, ΔBP represents anamount of change in blood pressure estimate, and BP_(cal) represents areference blood pressure at the time of calibration. Here, BP representsMAP, DBP, and SBP.

FIG. 5A is a flowchart illustrating a method of estimating bloodpressure according to one example embodiment. FIG. 5B is a flowchartillustrating one embodiment of an operation 540 of estimating a relativechange of a feature in FIG. 5A.

FIGS. 5A and 5B illustrate a method of estimating blood pressureaccording to an example embodiment. Various embodiments have beendescribed above in detail, and hence a brief description of the methodwill be given hereinafter.

The apparatus 100/200 for estimating blood pressure may receive a bloodpressure estimation request in operation 510. The apparatus 100/200 mayprovide an interface to a user and receive the blood pressure estimationrequest input by the user through the interface. Alternatively, theapparatus 100/200 may establish a communication connection with anexternal device 250 to receive a blood pressure estimation request fromthe external device 140. In this case, the external device may be asmartphone, a tablet personal computer (PC), or the like, which the usercarries, and the user may control an operation of the apparatus forestimating blood pressure through a device having a superior interfaceperformance or computing performance.

Then, the apparatus 100/200 for estimating blood pressure may control asensor 110 internally mounted for blood pressure estimation to acquire abio-signal from the user or receive a bio-signal from an external sensorin operation 520. In this case, the sensor 110 mounted in the apparatus100/200 and the external sensor 250 may acquire various bio-signals,such as a PPG signal, an ECG signal, an EMG signal, and a BCG signal,from various body parts (e.g., wrist, chest, finger, and the like) ofthe user.

Then, cardiovascular features may be extracted by analyzing the acquiredbio-signal in operation 530. In this case, the cardiovascular featuresmay include a first feature including a CO feature and a second featureincluding a TPR feature. In this case, the apparatus 100/200 may acquireheartbeat information from bio-signals, a shape of a bio-signalwaveform, the time and amplitude at a maximum point of a bio-signal, thetime and amplitude at a minimum point of a bio-signal, the area of abio-signal waveform, an elapsed time of a bio-signal, the amplitude andtime information of a constituent pulse waveform of a bio-signal, andcharacteristic point information, such as information on internaldivision points of pieces of the obtained information, and may extractthe cardiovascular features using the acquired characteristic pointinformation. In this case, the first feature and the second feature maybe extracted for each type of blood pressure to be estimated by usingdifferent characteristic points or combining two or more differentcharacteristic points.

Then, a relative change in cardiovascular feature at the time of bloodpressure estimation with respect to the cardiovascular feature at thetime of calibration may be estimated in operation 540. In operation 540,an initial value of a feature may be a value of the feature at acalibration time and a changed value of the feature may be a value ofthe feature at an extraction time.

For example, referring to FIG. 5B, the apparatus 100/200 for estimatingblood pressure may calculate a first change amount of the first featurewith respect to the first feature at the time of calibration inoperation 541 and calculate a second change amount of the second featurewith respect to the second feature at the time of calibration inoperation 542.

Then, a third change amount may be calculated by multiplying the firstchange amount and the second change amount in operation 543. The thirdchange amount calculated as described above may serve to correct a bloodpressure estimation error which may occur according to the mechanism ofblood pressure change, such as a high-intensity aerobic exercise.

Then, a first change rate, a second change rate, and a third change ratemay be calculated by normalizing the first change amount, the secondchange amount, and the third change amount, respectively in operation544. In this case, normalization may be performed based on at least oneof a first reference feature and a second reference feature at the timeof calibration. For example, the first change amount may be normalizedbased on the first reference feature and the second change amount may benormalized based on the second reference feature. In addition, the thirdchange amount may be normalized using a value obtained by multiplyingthe first reference feature and the second reference feature.

Referring back to FIG. 5A, the apparatus 100/200 for estimating bloodpressure may estimate blood pressure based on a relative change in eachof the first feature and the second feature in operation 550. At thistime, the relative changes in the first feature and the second featuremay be the change amounts calculated in operation 543 or the changerates calculated in operation 544. In one example, the blood pressuremay be estimated by linearly combining the change amounts or the changerates. In another example, a weight is applied to each of the changeamounts or each of the change rates and the weighted change amounts orchange rates are linearly combined, and a scaling factor may be appliedto the linear combination result to estimate the blood pressure. Each ofthe weights and the scaling factor may be defined differently accordingto the type of blood pressure to be estimated and/or the user'scharacteristic.

Meanwhile, the apparatus 100/200 for estimating blood pressure mayindependently estimate MAP, DBP, and SBP by using the relative change ineach feature extracted according to the type of blood pressure to beestimated or by controlling each of the weights and/or scaling factor.Alternatively, MAP may be estimated in substantially the same manner asdescribed above, and DBP and SBP may be estimated based on the estimatedMAP and a pulse pressure. For example, when the MAP is estimated, afirst value and a second value are calculated based on a pulse pressure,DBP may be estimated based on the MAP and the first value, and SBP maybe estimated by applying the second value to the DBP. The blood pressureestimation result obtained as described above may be provided to theuser in various ways.

FIGS. 6A and 6B are diagrams for describing a wearable device accordingto one example embodiment. The above-described various embodiments ofthe apparatus for estimating blood pressure may be mounted in asmartwatch or a smart band-type wearable device worn on a wrist asillustrated in FIG. 6A. However, the embodiment is not limited thereto,such that the apparatus for estimating blood pressure may be mounted ina smartphone, a tablet PC, a desktop PC, a notebook PC, and the like.

Referring to FIGS. 6A and 6B, the wearable device 600 may include a mainbody 610 and a strap 620.

The strap 620 may be configured to be flexible and be bendable to wraparound a user's wrist or be separated from the user's wrist.Alternatively, the strap 620 may be configured in the form of anundivided band. In this case, the strap 620 may be filled with air orhave an air bag to have elasticity according to a change in pressureapplied to the wrist and may transmit the pressure change of the wristto the main body 610.

A battery may be embedded in the main body 610 or the strap 620 tosupply power to the wearable device 600.

In addition, one or more sensors to measure various bio-signals may bemounted inside the main body 610. For example, a pulse wave sensor 611may be mounted on a rear surface of the main body 610, which is incontact with an object OBJ, for example, a wrist area, in such a mannerto be exposed to the object OBJ. The pulse wave sensor 611 may include alight source 611 a configured to emit light to the object OBJ and adetector 611 b configured to measure a pulse wave signal by detectinglight scattered or reflected from the object OBJ. In this case, thelight source 611 a may include at least one of a light emitting diode(LED), a laser diode, and a phosphor, and may be formed by one or two ormore arrays. The light source 611 a formed by two or more arrays may beconfigured to emit light rays of different wavelengths. In addition, thedetector 611 b may include a photodiode, an image sensor, and the like,and may be formed by one or two or more arrays.

A processor 612 configured to estimate blood pressure based on thebio-signals received from the pulse wave sensor 611 and/or the externalsensors may be mounted in the main body 610 of the wearable device 600.The processor 612 may generate a control signal in response to theuser's blood pressure estimation request and control the pulse wavesensor 611, and may control a communication interface 613 to receivebio-signals from the external sensor as needed.

The communication interface 613 may be mounted inside the main body 610and transmit and receive necessary information by communicating with theexternal device 250 under the control of the processor 612. For example,the communication interface 613 may receive bio-signals from externalsensors, such as an ECG sensor, an EMG sensor, a BCG sensor, and thelike, which are configured to measure the bio-signals. In addition, thecommunication interface 613 may receive the blood pressure estimationrequest from a portable terminal of the user. Also, the communicationinterface 613 may transmit the extracted characteristic points orfeature information to the external device to enable the external deviceto estimate blood pressure. Additionally, the communication interface613 may transmit the blood pressure estimation result to the externaldevice 250 to display the result to the user or allow the result to beutilized for various purposes, such as blood pressure historymanagement, disease research, and the like. Further, the communicationinterface 613 may receive a blood pressure estimation equation orreference information, such as reference blood pressure measured by ablood pressure measurement device, from the external device.

When the bio-signals are received from the pulse wave sensor 611 and/orthe external sensors, the processor 612 may extract a CO feature and aTPR feature from the received bio-signals. For example, as describedabove, the processor 612 may acquire various characteristic points byanalyzing the pulse wave signal and extract features by combining theacquired characteristic points. In this case, the processor 612 mayextract the CO feature and the TPR feature using MAP, SBP, and DBP.

The processor 612 may estimate relative changes in the CO feature andthe TPR feature with respect to those at the time of calibration inconsideration of a fact that absolute values of the CO feature and TPRfeature at the time of blood pressure estimation are difficult toacquire, and may estimate blood pressure using the estimated relativechanges, which has been described in detail above.

The wearable device 600 may further include an operator 615 and adisplay 614, which are mounted in the main body 610.

The operator 615 may receive a control command of the user and transmitthe control command to the processor 612 and may include a power buttonfor inputting a command to power on/off the wearable device 600.

The display 614 may provide a variety of information related to thedetected blood pressure to the user under to control of the processor612. For example, the display 614 may display additional information,such as detected blood pressure, alarm, warning, and the like, to theuser using various visual/non-visual methods.

While not restricted thereto, an example embodiment can be embodied ascomputer-readable code on a computer-readable recording medium. Thecomputer-readable recording medium is any data storage device that canstore data that can be thereafter read by a computer system. Examples ofthe computer-readable recording medium include read-only memory (ROM),random-access memory (RAM), CD-ROMs, magnetic tapes, floppy disks, andoptical data storage devices. The computer-readable recording medium canalso be distributed over network-coupled computer systems so that thecomputer-readable code is stored and executed in a distributed fashion.Also, an example embodiment may be written as a computer programtransmitted over a computer-readable transmission medium, such as acarrier wave, and received and implemented in general-use orspecial-purpose digital computers that execute the programs. Moreover,it is understood that in example embodiments, one or more units of theabove-described apparatuses and devices can include circuitry, aprocessor, a microprocessor, etc., and may execute a computer programstored in a computer-readable medium.

The foregoing example embodiments are merely example and are not to beconstrued as limiting. The present teaching can be readily applied toother types of apparatuses. Also, the description of the exampleembodiments is intended to be illustrative, and not to limit the scopeof the claims, and many alternatives, modifications, and variations willbe apparent to those skilled in the art.

What is claimed is:
 1. A wearable device comprising: a photoplethysmogram (PPG) sensor comprising a light source configured to emit light to a user and a detector configured to obtain a PPG signal by detecting light scattered or reflected from the user; at least one processor is configured to: extract a cardiac output (CO) feature and a total peripheral resistance(TPR) feature from the PPG signal measured at an extraction time; calculate a first difference between an initial value of the CO feature at a calibration time before the extraction time and a changed value of the CO feature at the extraction time; calculate a second difference between the initial value of the TPR feature at the calibration time and a changed value of the TPR feature at the extraction time; calculate a product of the first difference and the second difference; and estimate blood pressure based on the first difference, the second difference, the product of the first difference and the second difference; and a display is configured to provide warning information when the estimated blood pressure falls outside a predetermined normal range.
 2. The wearable device of claim 1, wherein the at least one processor is further configured to acquire, from the PPG signal, at least one information of heartbeat information, information on a shape of a waveform of the PPG signal, area information of the waveform, time and amplitude information at a maximum point of the PPG signal, time and amplitude information at a minimum point of the PPG signal, and amplitude and time information of a constituent pulse waveform of the PPG signal, and extract the CO feature and the TPR feature based on the at least one information.
 3. The wearable device of claim 2, wherein the at least one processor is further configured to estimate a mean arterial pressure (MAP), a diastolic blood pressure (DBP), and a systolic blood pressure (SBP) based on the CO feature and the TPR feature.
 4. The wearable device of claim 1, wherein the at least one processor is further configured to normalize each of the first difference, the second difference, and the product of the first difference and the second difference, based on at least one of the initial value of the CO feature and the initial value of the TPR feature at the calibration time, to obtain normalization results, and estimate the blood pressure based on each of the normalization results.
 5. The wearable device of claim 4, wherein the at least one processor is further configured to apply a weight to each of the normalization results to obtain weighted results, combine the weighted results to obtain a combination result, and estimate the blood pressure by applying a scaling factor to the combination result.
 6. The wearable device of claim 5, wherein the at least one processor is further configured to determine the scaling factor based on at least one of a reference mean arterial pressure (MAP), a reference systolic blood pressure (SBP), and a reference diastolic blood pressure (DBP) of the user, which are measured at the calibration time, and a result of combining the reference SBP and the reference DBP.
 7. The wearable device of claim 5, wherein the at least one processor is configured to independently estimate MAP, SBP, and DBP by adjusting at least one of the weight and the scaling factor.
 8. The wearable device of claim 5, wherein the at least one processor is further configured to: estimate a mean arterial pressure (MAP) of the user by adjusting at least one of the weight and the scaling factor; obtain a pulse pressure from the PPG signal; and estimate a diastolic blood pressure (DBP) and a systolic blood pressure (SBP) of the user based on the MAP, the pulse pressure, and the adjusted at least one of the weight and the scaling factor.
 9. The wearable device of claim 8, wherein the at least one processor is further configured to calculate a first value and a second value based on the pulse pressure, estimate the DBP based on the MAP and the first value, and estimate the SBP based on the DBP and the second value.
 10. The wearable device of claim 1, further comprising a communication interface configured to, when the user measures a reference blood pressure for calibration through an external electrical device at the calibration time, receive the reference blood pressure from the external electrical device.
 11. A method of operating a wearable device, the method comprising: measuring, by a photoplethysmogram (PPG) sensor, a PPG signal from a user, wherein the PPG sensor comprising a light source configured to emit light to the user and a detector configured to obtain the PPG signal by detecting light scattered or reflected from the user; extracting, by at least one processor, a cardiac output (CO) feature and a total peripheral resistance(TPR) feature from the PPG signal measured at an extraction time; calculating, by the at least one processor, a first difference between an initial value of the CO feature at a calibration time before the extraction time and a changed value of the CO feature at the extraction time; calculating, by the at least one processor, a second difference between the initial value of the TPR feature at the calibration time and a changed value of the TPR feature at the extraction time; calculating, by the at least one processor, a product of the first difference and the second difference; estimating, by the at least one processor, blood pressure based on the first difference, the second difference, the product of the first difference and the second difference; and providing, by a display, warning information when the estimated blood pressure falls outside a predetermined normal range.
 12. The method of claim 11, wherein the estimating the blood pressure comprises estimating a mean arterial pressure (MAP), a diastolic blood pressure (DBP), and a systolic blood pressure (SBP) based on the changes in the CO feature and the TPR feature.
 13. The method of claim 11, wherein the estimating the changes comprises normalizing each of the first difference, the second difference, and the product of the first difference and the second difference based on at least one of the initial value of the CO feature and the initial value of the TPR feature at the calibration time, to obtain normalization results.
 14. The method of claim 13, wherein the estimating the blood pressure comprises applying a weight to each of the normalization results to obtain weighted results, combining the weighted results to obtain a combination result, and estimating the blood pressure by applying a scaling factor to the combination result.
 15. The method of claim 14, further comprising determining the scaling factor based on at least one of a reference MAP, a reference systolic blood pressure (SBP), and a reference diastolic blood pressure (DBP), which are measured at the calibration time, and a result of combining the reference SBP and the reference DBP.
 16. The method of claim 14, wherein the estimating the blood pressure comprises: estimating a mean arterial pressure (MAP) of the user by adjusting at least one of the weight and the scaling factor; obtaining a pulse pressure from the PPG signal; and estimating a diastolic blood pressure (DBP) and a systolic blood pressure (SBP) of the user based on the MAP, the pulse pressure, and the adjusted at least one of the weight and the scaling factor. 